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A Traffic Event Detection Method Based on Random Forest and Permutation Importance

Author

Listed:
  • Ziyi Su

    (School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210019, China)

  • Qingchao Liu

    (Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China)

  • Chunxia Zhao

    (School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210019, China)

  • Fengming Sun

    (School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210019, China)

Abstract

Although the video surveillance system plays an important role in intelligent transportation, the limited camera views make it difficult to observe many traffic events. In this paper, we collect and combine the traffic flow variables from the multi-source sensors, and propose a PITED method based on Random Forest (RF) and Permutation importance (PI) for traffic event detection. This model selects the suitable traffic flow variables by means of permutation arrangement of importance, and establishes the whole process of acquisition, preprocessing, quantization, modeling and evaluation. Moreover, the real traffic data are collected and tested in this paper for evaluating the experiment performance, including the miss/false rate of traffic event, and average detection time. The experimental results show that the detection rate is more than 85% and the false alarm rate is less than 3%. It means the model is effective and efficient in the practical application regardless of both workdays and holidays.

Suggested Citation

  • Ziyi Su & Qingchao Liu & Chunxia Zhao & Fengming Sun, 2022. "A Traffic Event Detection Method Based on Random Forest and Permutation Importance," Mathematics, MDPI, vol. 10(6), pages 1-14, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:6:p:873-:d:767572
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    References listed on IDEAS

    as
    1. Qiao Meng & Huansheng Song & Yu’an Zhang & Xiangqing Zhang & Gang Li & Yanni Yang, 2020. "Video-Based Vehicle Counting for Expressway: A Novel Approach Based on Vehicle Detection and Correlation-Matched Tracking Using Image Data from PTZ Cameras," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-16, March.
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